{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load libraries and data:\n",
    "The data will be the one exported from the 'exploration_and_feature_engineering' file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 12, 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train_modified.csv\")\n",
    "test = pd.read_csv(\"test_modified.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 34)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Item_Identifier               object\n",
       "Item_MRP                     float64\n",
       "Item_Outlet_Sales            float64\n",
       "Item_Visibility              float64\n",
       "Item_Weight                  float64\n",
       "Outlet_Identifier             object\n",
       "Item_Visibility_MeanRatio    float64\n",
       "Outlet_Years                   int64\n",
       "Item_Fat_Content_0           float64\n",
       "Item_Fat_Content_1           float64\n",
       "Item_Fat_Content_2           float64\n",
       "Outlet_Location_Type_0       float64\n",
       "Outlet_Location_Type_1       float64\n",
       "Outlet_Location_Type_2       float64\n",
       "Outlet_Size_0                float64\n",
       "Outlet_Size_1                float64\n",
       "Outlet_Size_2                float64\n",
       "Outlet_Type_0                float64\n",
       "Outlet_Type_1                float64\n",
       "Outlet_Type_2                float64\n",
       "Outlet_Type_3                float64\n",
       "Item_Type_Combined_0         float64\n",
       "Item_Type_Combined_1         float64\n",
       "Item_Type_Combined_2         float64\n",
       "Outlet_0                     float64\n",
       "Outlet_1                     float64\n",
       "Outlet_2                     float64\n",
       "Outlet_3                     float64\n",
       "Outlet_4                     float64\n",
       "Outlet_5                     float64\n",
       "Outlet_6                     float64\n",
       "Outlet_7                     float64\n",
       "Outlet_8                     float64\n",
       "Outlet_9                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print train.shape\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aarshay/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#Mean based:\n",
    "mean_sales = train['Item_Outlet_Sales'].mean()\n",
    "\n",
    "#Define a dataframe with IDs for submission:\n",
    "base1 = test[['Item_Identifier','Outlet_Identifier']]\n",
    "base1['Item_Outlet_Sales'] = mean_sales\n",
    "\n",
    "#Export submission file\n",
    "base1.to_csv(\"alg0.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to fit and generate submission file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Define target and ID columns:\n",
    "target = 'Item_Outlet_Sales'\n",
    "IDcol = ['Item_Identifier','Outlet_Identifier']\n",
    "from sklearn import cross_validation, metrics\n",
    "def modelfit(alg, dtrain, dtest, predictors, target, IDcol, filename):\n",
    "    #Fit the algorithm on the data\n",
    "    alg.fit(dtrain[predictors], dtrain[target])\n",
    "        \n",
    "    #Predict training set:\n",
    "    dtrain_predictions = alg.predict(dtrain[predictors])\n",
    "\n",
    "    #Perform cross-validation:\n",
    "    cv_score = cross_validation.cross_val_score(alg, dtrain[predictors], dtrain[target], cv=20, scoring='mean_squared_error')\n",
    "    cv_score = np.sqrt(np.abs(cv_score))\n",
    "    \n",
    "    #Print model report:\n",
    "    print \"\\nModel Report\"\n",
    "    print \"RMSE : %.4g\" % np.sqrt(metrics.mean_squared_error(dtrain[target].values, dtrain_predictions))\n",
    "    print \"CV Score : Mean - %.4g | Std - %.4g | Min - %.4g | Max - %.4g\" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score))\n",
    "    \n",
    "    #Predict on testing data:\n",
    "    dtest[target] = alg.predict(dtest[predictors])\n",
    "    \n",
    "    #Export submission file:\n",
    "    IDcol.append(target)\n",
    "    submission = pd.DataFrame({ x: dtest[x] for x in IDcol})\n",
    "    submission.to_csv(filename, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "RMSE : 1128\n",
      "CV Score : Mean - 1129 | Std - 44.16 | Min - 1074 | Max - 1218\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b4f3650>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KOikijpziNbTxBQAA05qYWKBly5YO9dp58+brhhuunJOY0ji0m6WN76htfNtKfL8g6caI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\nP6mL9wQuAs60/UAOMUsmaQFwAnAo1bqq19m+MLeYoTySzqb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b4e3f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target]+IDcol]\n",
    "# print predictors\n",
    "alg1 = LinearRegression(normalize=True)\n",
    "modelfit(alg1, train, test, predictors, target, IDcol, 'alg1.csv')\n",
    "coef1 = pd.Series(alg1.coef_, predictors).sort_values()\n",
    "coef1.plot(kind='bar', title='Model Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "RMSE : 1129\n",
      "CV Score : Mean - 1130 | Std - 44.6 | Min - 1076 | Max - 1217\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10cf61450>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JkqQOhmVJkiSpg2FZkiRJ6mBYliRJkjoYliVJkqQOhmVJkiSpg2FZkiRJ6mBYliRJkjoY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\nWtw23lGqaT+JtGj+Z+AjZvaZtppBXpSSMPwJ8F4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6QxKPPEfEub08+sFshA0EnSHcMIKg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce300d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target]+IDcol]\n",
    "alg2 = Ridge(alpha=0.05,normalize=True)\n",
    "modelfit(alg2, train, test, predictors, target, IDcol, 'alg2.csv')\n",
    "coef2 = pd.Series(alg2.coef_, predictors).sort_values()\n",
    "coef2.plot(kind='bar', title='Model Coefficients')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "RMSE : 1058\n",
      "CV Score : Mean - 1091 | Std - 45.42 | Min - 1003 | Max - 1186\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d687250>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAEAxSH4BAABQDJJfAAAAFIPkFwAAAMUg+QUAAEAxSH4BAABQDJJfAAAAFIPkFwAAAMUg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\npdQbOflNKaWUUkq98f8IHGAL+AbzEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d5a1d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target]+IDcol]\n",
    "alg3 = DecisionTreeRegressor(max_depth=15, min_samples_leaf=100)\n",
    "modelfit(alg3, train, test, predictors, target, IDcol, 'alg3.csv')\n",
    "coef3 = pd.Series(alg3.feature_importances_, predictors).sort_values(ascending=False)\n",
    "coef3.plot(kind='bar', title='Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "RMSE : 1071\n",
      "CV Score : Mean - 1096 | Std - 43.3 | Min - 1027 | Max - 1172\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10da94b10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mPbSqvivJu7r7IROXxsDmFxJ9Z5IXJfm1hV1XuVaC7aaqTshsmuPbutstjg8R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\nFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10daa6690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = ['Item_MRP','Outlet_Type_0','Outlet_5','Outlet_Years']\n",
    "alg4 = DecisionTreeRegressor(max_depth=8, min_samples_leaf=150)\n",
    "modelfit(alg4, train, test, predictors, target, IDcol, 'alg4.csv')\n",
    "coef4 = pd.Series(alg4.feature_importances_, predictors).sort_values(ascending=False)\n",
    "coef4.plot(kind='bar', title='Feature Importances')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Model:\n",
    "Note: random forest models are not 100% replicable. So the outputs might differ very slightly but should be around the ballpark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "RMSE : 1073\n",
      "CV Score : Mean - 1084 | Std - 43.79 | Min - 1020 | Max - 1160\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10df14d10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAAAoBskvAAAAikHyCwAAgGKQ/AIAAKAYJL8AAAAoBskvAAAAikHyCwAAgGKQ/AIAAKAY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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ddc7ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target]+IDcol]\n",
    "alg5 = RandomForestRegressor(n_estimators=200,max_depth=5, min_samples_leaf=100,n_jobs=4)\n",
    "modelfit(alg5, train, test, predictors, target, IDcol, 'alg5.csv')\n",
    "coef5 = pd.Series(alg5.feature_importances_, predictors).sort_values(ascending=False)\n",
    "coef5.plot(kind='bar', title='Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model Report\n",
      "RMSE : 1068\n",
      "CV Score : Mean - 1083 | Std - 43.77 | Min - 1020 | Max - 1162\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10df12450>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fAAAAFIPkFwAAAMUg+QUAAEAxSH4BAABQDJJfAAAAFIPkFwAAAMUg+QUAAEAxSH4BAABQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f81af10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictors = [x for x in train.columns if x not in [target]+IDcol]\n",
    "alg6 = RandomForestRegressor(n_estimators=400,max_depth=6, min_samples_leaf=100,n_jobs=4)\n",
    "modelfit(alg6, train, test, predictors, target, IDcol, 'alg6.csv')\n",
    "coef6 = pd.Series(alg6.feature_importances_, predictors).sort_values(ascending=False)\n",
    "coef6.plot(kind='bar', title='Feature Importances')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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